ENERGY ECONOMICS REVIEW
Year:2019 | Volume:15 | Issue:62|Papers Count:8

Today there is consensus regarding the importance of predicting the price of energy carriers for economic development, including natural gas as a preferred energy source on environmental grounds. In this paper we analyze the nonlinear behavior of natural gas prices over time by use of the Grey Method (GM), Developed Grey Method (DGM), Fractal Discrete Grey Method (FDGM) and a compound Method II (IFS-AF). Our results indicate the effectiveness of these methods in predicting gas prices. In addition, due to the fractal structure of natural gas prices in the period under review, the results show that the prediction error of the compound method is always less than 2 percentage points. We note that due to the flexibility of the method, a combination of Grey and Fractal methods produce excellent results.

The oil and gas industry has huge financial turnover and major projects, especially in the upstream areas, require substantial financing. Hence, securing financing is one of the most important requirements for successful implementation of projects in this industry. In this research, we adopt a descriptive approach and rely on the opinion of experts, to identify and prioritize strategies for improving the financing system in the oil and gas industry. The results of the research show that the success of strategies for improving the financing system in the country's oil and gas industry depends on the three factors of regulatory infrastructure, structural factors and information transparency. In other words, by taking appropriate actions on these three factors, we can improve the financing system in the country's oil and gas industry. Based on our results, improving the regulatory infrastructure has the highest priority, followed by improving structural factors and information transparency.

By studying the complex process of energy policy formulation for the oil and gas sector in Iran, we notice that these policies are not fully implemented due to inefficiency of the executive model. In this paper, we use the qualitative research of data theory, to analyze the situation through a combination of semi-structured interviews and study of available data. We use a snowball (chain reference) sampling method to select our interviewees. While we had selected 30 persons to be interviewed, as we saw repetitive responses beyond the 10th person we only interviewed 20 persons. Data collection has started in December 2016. The interview began with questions about "the reasons for not implementing the energy policies of the country in the field of oil and gas" (open question), and the rest of the questions were based on the responses of the interviewee. All interviews were taken into account and analyzed several times to extract key points. Finally, the model was designed using the categories and components derived from the interviews, and the results were compared with other models of policy implementation. The results of our study is derived from the use of this composite model.

Today, factors such as increasing energy efficiency, use of alternative fuels, and application of new fuel standards and new environmental laws in OECD countries have led to wide-range of changes in the structure of demand for fossil fuels. Given the importance of light derivatives, namely gasoline, jet fuel and diesel in the demand basked of OECD countries we focus on these products. We use the least squares dynamic method (DOLS) to estimate the demand function for jet fuel, gasoline and diesel. The results of the estimation indicate a high and meaningful level of income elasticity of demand, while price elasticity is less pronounced, but still significant. We also notice that incentive and punitive policies, strict rules and environmental constraints and technical advancements have an important impact on the demand for these products. Finally, in order to predict the demand for these three products in the OECD countries for 2025, we first estimate the prices of these fuels, and then use these prices to estimate the demand function of these products.

The prediction of economic series with high volatility and high uncertainty-such as natural gas prices-is always a challenge in econometric models, because the use of traditional linear modeling models does not allow us to predict complex and nonlinear time series. Regarding the prediction of natural gas prices, findings point to superiority of the neural network compared to regression models. Nevertheless, the main challenge of this method-the possibility of overlapping and noise of data from the system-has kept the choice for an optimal method open. In this study we use the Kriging interpolation to predict the price of natural gas. For this purpose, after identifying the effective parameters, sampling and normalizing them, we created a Kriging predicting functions and improved it with the Nelder-Mead optimization technique. The results of the study show that the Kriging metamodel provides a more accurate prediction than the artificial neural network prediction model. Our research findings also suggest that the Neldar-Mead optimization algorithm has somewhat improved the predicted results. However, theextent of this improvement is not remarkable.

Improving the quality of the environment is a desired goal of any economy. In this regard, given the important role of the transportation sector in the emission of pollutants, policy makers should focus on identifying the factors affecting CO2 emissions in the transport sector. This study examines the factors affecting the differences of CO2 emissions in the transportation sector in Iranian provinces by applying the extended STIRPAT model and quantile regression and using the provincial-level data for the period 2006 to 2015. Evidence shows that the Yazd and Semnan provinces has the highest per capita logarithms of CO2 emissions equal to 0. 51 and 0. 5 respectively, and Golestan province has the lowest value equal to 0. 3. Evidence of estimation shows that urbanization, energy intensity and production have a positive and significant effect and the effect of population on CO2 emissions is negative and significant. Also, according to quantile regression, as we move toward a high CO2 emission level, the effect of energy intensity increases and the effect of urbanization decreases. Based on this, improving urban infrastructure to maximize the benefits of high intensity of occupancy, improving production technology and improving the quality of the transportation system are effective steps for reducing carbon dioxide emissions in the transport sector.

The use of agricultural machinery, fuel, fertilizers, and pesticides render agriculture dependent on energy and have negative environmental effects on water, air and land. Therefore, it is necessary to study the process of energy consumption for producting the different products and take the necessary measures to increase the efficiency and decrease the environmental effect of these inputs. In this study, we calculate the pattern of energy consumption in dryland rape, using energy ratio, energy efficiency, energy intensity and net energy addition and the environmental effects in three districts of Ilam province using the life cycle evaluation. In order to conduct the study, we obtained the required data by using the questionnaire method to interview farmers and related departments. The results of this study showed that the average ratio of the energy for the dryland rape production in Chardavol, Darrehshahr, and Abdanan districts was respectivelyt 7. 91, 7. 67, and 6. 91. Based on the results of the life cycle assessment, diesel fuel had the maximum effect on reduction of inorganic sources, ozone layer depletion and water contanimation. Direct emissions had the greatest impact on acidification, river pollution, global warming and human poisoning.

Literature review related to nonrenewable resources shows that technological improvements have considerable effects on resource depletion and decreasing operational cost. Therefore it is assumed that technology is the most important and influential variables in the production function and utilization cost of these resources. In this study, we assess the long term effect of technology diffusion on average cost in oil industry upstream activities. Desired variables in this research include: oil and gas production, upstream average cost, depletion effect and technology diffusion in the upstream sector. We use cointegration test results, using error vector correction model to study the long term relationship between variables for 1967 to 2017 time frame. Johansson test results show that there is a long term cointegrated relationship between variables of the model. Using instantaneous reaction functions, we calculate the effect of technology improvement on upstream cost reduction, oil production increase and increase of evacuation effect. At the end, we use variance analysis, to calculate the share of each variable variation in changing average cost in each period. The highest variation value relates to the technology variable. The share of this variable has been ascending up to the 15th period of time. The long term adjustment coefficient for all variables is found to have the desired sign and be negative. In this model the technology coefficient is meaningless because the impact of technology compared to the exterior long term relationship parameters is weak.